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2010
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Multi-task learning for boosting with application to web search ranking

11 years 5 months ago
Multi-task learning for boosting with application to web search ranking
In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing the specifics of each learning task with task-specific parameters and the commonalities between them through shared parameters. This enables implicit data sharing and regularization. We evaluate our learning method on web-search ranking data sets from several countries. Here, multitask learning is particularly helpful as data sets from different countries vary largely in size because of the cost of editorial judgments. Our experiments validate that learning various tasks jointly can lead to significant improvements in performance with surprising reliability. Categories and Subject Descriptors I.2.6 [Artificial intelligence]: Learning; H.3.3 [Information storage and retrieval]: Information search and retrieval General Terms Algorithms
Olivier Chapelle, Pannagadatta K. Shivaswamy, Srin
Added 15 Aug 2010
Updated 15 Aug 2010
Type Conference
Year 2010
Where KDD
Authors Olivier Chapelle, Pannagadatta K. Shivaswamy, Srinivas Vadrevu, Kilian Q. Weinberger, Ya Zhang, Belle L. Tseng
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